Predictive Modeling Inputs
| Data Input | Predictive Signal | Risk Indicator |
|---|---|---|
| ER visits | Unmanaged chronic conditions; lack of PCP | High |
| Medication non-adherence | Poor disease management | High |
| Multiple chronic conditions | Comorbidity risk | Very high |
| Recent hospitalization | Readmission risk | High |
| Specialist visits without PCP | Fragmented care | Medium |
| Gaps in preventive care | Undetected conditions | Medium |
Intervention Targeting
Predictive models identify members who are likely to become high-cost in the next 12 months. These members are prioritized for proactive outreach — care management, disease management enrollment, medication adherence support, and care coordination. Early intervention for these members can prevent costly hospitalizations and ER visits.
Model Accuracy
IQ Insight
Modern predictive models can identify 70–80% of next year's high-cost members from current claims data. This means that for every 10 members who will generate catastrophic claims next year, 7–8 can be identified and targeted for intervention today. The 20–30% who are not identified are typically members with new conditions or sudden health events that cannot be predicted from historical data.